Machining process energy consumption modelling using response surface methodology and neural network

Li, Xiaoxia; Lu, Junyuan

doi:10.1109/cscwd.2017.8066724

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Further, the proposed model was validated through an industrial case study in the European machining industry. Li and Lu (2017) developed an energy consumption model based on artificial neural network (ANN) and response surface methodology (RSM). In their study, a two-level optimization model was proposed to demonstrate the relationship between machining parameters and energy consumption.…”

Section: Review Methodologymentioning

confidence: 99%

“…Previously published studies show that there are opportunities for ML techniques to be applied in sustainable manufacturing, i.e. sustainable planning scheduling and predictive maintenance (Li and Lu, 2017) . But a comprehensive review and bibliometric analysis which reports the opportunities of ML techniques in sustainable manufacturing are still missing in the available literature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Jamwal

Agrawal

Sharma

et al. 2021

JEIM

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PurposeThe role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).Design/methodology/approachIn the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.FindingsThe present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.Originality/valueA comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

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Section: Review Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Jamwal

Agrawal

Sharma

et al. 2021

JEIM

View full text Add to dashboard Cite

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A State of the Art on Simulation and Modelling Methods in Machining: Future Prospects and Challenges

Korkmaz

Gupta

2022

Arch Computat Methods Eng

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Simulation modelling methods have gained dramatic acceleration in the last years among academic environments and industry-driven enterprises. Primary reason is that such models have great potential in predicting of machining process parameters. Therefore, tis study evaluates the place and capability of these models in fundamental machining operations. In this direction, Finite Element Modelling Methods are discussed by questioning their contributions to the process performance. Despite numerous positive aspects, development of a successful model is highly difficult owing to the complexity of machining environment with variation of thermo-mechanical effect, tribological conditions, interaction of process variables and high deformation rate of materials etc. Therefore, a critical assessment of the merits and drawbacks of each method associating with their basic phenomena has been investigated. Predictive models basically aim to estimate the machinability characteristics such as stress–stain rates, cutting forces and temperatures etc. Nevertheless, practical applications require correlations between these characteristics and performance outcomes such as surface integrity of part, tool wear index, chip morphology, dimensional accuracy etc. In the end, the molecular dynamics and smoothed particle hydrodynamics have been discussed. Thus, this paper is expected to contribute to up-to-date studies by criticizing the key findings of the predictive models in machining processes.

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Energy consumption measurements based on numerical integration

Votava

Kynčl

Straka

2018

2018 19th International Scientific Conference on Electric Power Engineering (EPE)

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Machining process energy consumption modelling using response surface methodology and neural network

Cited by 3 publications

References 17 publications

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

A State of the Art on Simulation and Modelling Methods in Machining: Future Prospects and Challenges

Energy consumption measurements based on numerical integration

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